Relative calibration for dosimetric devices

ABSTRACT

Calibrating the dose response of an image acquisition device comprises comparing a first self-calibration curve to a second self-calibration curve to determine the relationship between the curves; modifying an acquired image based on the at least one difference; and applying an initial calibration to the acquired image, whereby the dose response of the image acquisition device is calibrated.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.11/181,057, filed Jul. 14, 2005, which is a divisional application ofU.S. Pat. No. 7,024,026. This application is also related to presentlypending U.S. patent application Ser. No. 11/282,241, filed Nov. 18,2005, entitled “SYSTEM OR METHOD FOR CALIBRATING A RADIATION DETECTIONMEDIUM”, which is a continuation of U.S. application Ser. No.10/949,436, filed Sep. 24, 2004, which is a continuation of theapplication for U.S. Pat. No. 6,934,653, filed May 27, 2003, which is acontinuation of the application for U.S. Pat. No. 6,675,116, filed Jun.1, 2001, claiming priority to U.S. provisional applications 60/234,745,filed Sep. 22, 2000, and 60/252,705, filed Nov. 22, 2000. Thisapplication is also related to the application for U.S. Pat. No.6,528,803, filed Jan. 21, 2000. This application is also related topending U.S. application Ser. No. 11/009,602, filed Dec. 10, 2004,entitled “OPTIMIZING IMAGE ALIGNMENT” and pending U.S. application Ser.No. 11/133,544, filed May 20, 2005, entitled “SYSTEM AND METHOD FORALIGNING IMAGES”, which is a continuation of the application for U.S.Pat. No. 6,937,751, filed Jul. 30, 2003 entitled. All of the foregoingrelated applications are fully incorporated herein by reference.

FIELD

The present invention relates to radiation dosimetry, and moreparticularly to methods and devices for efficiently performing radiationdose calibrations associated with radiotherapy.

BACKGROUND

An important use of radiotherapy, and in particular intensity-modulatedradiation therapy (IMRT), is the destruction of tumor cells. In the caseof ionizing radiation, tumor destruction depends on the “absorbed dose”,i.e., the amount of energy deposited within a tissue mass. Radiationphysicists normally express the absorbed dose in cGy units or centigray.One cGy equals 0.01 J/kg.

Radiation dosimetry generally describes methods to measure or predictthe absorbed dose in various tissues of a patient undergoingradiotherapy. Accuracy in predicting and measuring absorbed dose is keyto effective treatment and prevention of complications due to over orunder exposure to radiation. Many methods exist for measuring andpredicting absorbed dose, but most rely on developing a calibration—acurve, lookup table, equation, etc.—that relates the response of adetection medium to the absorbed dose. Useful detection media are knownto those skilled in the art and include radiation-sensitive films andthree-dimensional gels (e.g., ‘BANG’ and ‘BANANA’ gels) which darken orchange color upon exposure to radiation. Other useful detection mediainclude electronic portal-imaging devices, Computed Radiography (CR)devices, Digital Radiography (DR) devices, and amorphous silicondetector arrays, which generate a signal in response to radiationexposure.

There are various known methods for developing a calibration curve. Forexample, U.S. Pat. No. 6,675,116, assigned to the assignee of thepresent application and fully incorporated herein by reference,discloses providing a detection medium that responds to exposure toionizing radiation, and preparing a calibration dose response pattern byexposing predefined regions of the detection medium to differentionizing radiation dose levels. The '116 patent further disclosesmeasuring responses of the detection medium in the predefined regions togenerate a calibration that relates subsequent responses to ionizingradiation dose. Different dose levels are obtained by differentiallyshielding portions of the detection medium from the ionizing radiationusing, for example, a multi-leaf collimator, a secondary collimator, oran attenuation block. Different dose levels can also be obtained bymoving the detection medium between exposures. The '116 patent furtherdiscloses a software routine fixed on a computer-readable medium that isconfigured to generate a calibration that relates a response of adetection medium to an ionizing radiation dose.

Methods such as those disclosed in the '116 patent require exposingdiscrete portions of the detection medium to different and known amountsof radiation using a linear accelerator or similar apparatus in order todevelop a calibration curve or lookup table. Typically about twelve, butoften as many as twenty-five, different radiation dose levels aremeasured in order to generate a calibration curve or look-up table.Generally, the accuracy of the calibration increases as the number ofmeasured radiation dose levels increases. However, the greater thenumber of measurements, the more expensive and time consuming thecalibration process becomes. Thus, it would be desirable to have asystem and method that provides calibration information by analyzing one“acquired image” obtained by applying a radiation therapy plan to aquality assurance device and capturing the radiation intensitydistribution.

Methods of correcting an acquired image so that a dosimetry acquisitionsystem that has been once calibrated will not have to be recalibratedeach time are known. For example, U.S. Pat. No. 6,528,803, assigned tothe assignee of the present application and fully incorporated herein byreference, teaches exposing portions of test films to an array ofstandard light sources to obtain an optical density step gradient, whichcan then be compared to a corresponding optical density step gradient onone or all of a set of calibration films. However, existing methods suchas those disclosed by the '803 patent require additional equipment andtime to gather data relating to the optical density step gradient. Insome cases, it would be desirable to have a system and method providingcalibration information for a subsequent acquired image that did notrequire extra equipment and that took a minimum amount of time even ifthis was only a “relative” calibration (expressed in percent) and not an“absolute” calibration (in dose and trace able to a national standard).

Further, it may also be desirable to have a system to evaluate theability of experimentally derived calibration curves to model the dosedistributions produced by a systems that create treatment plans, andother predictions of dose distribution, in order to determine wheredifferences occur by modeling inaccuracies as opposed to trueexperimental differences.

BRIEF SUMMARY

According to an embodiment, a system for calibrating the dose responseof an image acquisition device comprises means for creating a dose mapthat indicates dosages that are included in a treatment plan, means forcreating an acquired image that includes representations of dosageintensities recorded from an application of the treatment plan; andmeans for creating a self-calibration curve that relates the dosages tothe dosage intensities.

Further, according to an embodiment, a system for calibrating the doseresponse of an image acquisition device comprises a firstself-calibration curve, a second self-calibration curve, an initialcalibration; and means for performing computations including: (1)determining at least one difference or fit between the firstself-calibration curve and the second self-calibration curve, (2)modifying an acquired image based on the at least one difference or fit;and (3) producing a relative calibration based on an application of theinitial calibration to the acquired image.

Further, according to an embodiment, a method for calibrating the doseresponse of an image acquisition device comprises comparing a firstself-calibration curve to a second self-calibration curve to determinethe relationship between the curves; modifying an acquired image basedon the at least one difference; and applying an initial calibration tothe acquired image, whereby the dose response of the image acquisitiondevice is calibrated.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram providing an overview of a system used in atleast one embodiment to build an IMRT self calibration curve (ISCC).

FIG. 2 is a process flow diagram describing a process flow for aninitial calibration process, according to an embodiment.

FIG. 3A is a process flow diagram describing a process of acquiringimages to be used in building an ISCC curve, according to an embodiment.

FIG. 3B is a process flow diagram describing an ISCC generation process,according to an embodiment.

FIG. 4 shows an example of an ISCC curve.

FIG. 5 is a process flow diagram describing a subsequent calibrationprocess.

FIG. 6A shows an example of a dose map.

FIG. 6B shows an example of an acquired image.

FIG. 7A shows an example of a dose map divided into small geometricareas and after a statistical function has been applied to the areas.

FIG. 7B shows an example of an acquired image divided into smallgeometric areas and after a statistical function has been applied to theareas.

FIG. 8A shows an example of a raw ISCC curve.

FIG. 8B shows an example of a post-processed ISCC curve.

FIG. 9A shows an example of a dose map in column format.

FIG. 9B shows an example of an acquired image in column format.

FIG. 10 shows an exemplary graph representing the correlationcoefficient for each set of corresponding columns in the mages shown inFIGS. 9A and 9B

FIG. 11A shows an exemplary image in column format representing a dosemap in which the columns are sorted according to the value of acorrelation coefficient.

FIG. 11B shows an exemplary image in column format representing anacquired image in which the columns are sorted according to the value ofa correlation coefficient.

FIG. 12A shows an exemplary raw ISCC curve based on plotting pixelvalues for areas of a dose map and acquired image that exceed acorrelation threshold

FIG. 12B shows an example of the ISCC curve of FIG. 12A afterpost-processing.

FIG. 13 shows an exemplary graph including an experimentally derivedcalibration curve and an ISCC curve.

FIG. 14 shows an exemplary graph on which is plotted the dosedifferences between an experimentally derived calibration curve and anISCC curve.

FIG. 15A shows an exemplary plot of a correlation statistic for tenequal sections of a calibration curve.

FIG. 15B shows an exemplary plot of a Root Mean Square statistic for tenequal sections of a calibration curve.

FIG. 16 shows an exemplary normalization curve relating to a portion ofa calibration curve.

DETAILED DESCRIPTION

A calibration is developed for a first treatment plan that relatesplanned dosages to a detection medium's response to an absorbed dose.Also, an IMRT self calibration curve (“ISCC” or “ISCC curve”), relatingdosage intensities in the first treatment plan to pixel intensities onan acquired image, is developed relating to the first treatment plan. AnISCC curve is then developed for a second treatment plan. A comparisonof the ISCC curve relating to the first treatment plan with the ISCCcurve relating to the second treatment plan allows adjustment of anacquired image relating to the second treatment plan so that thecalibration may be used to provide calibration information with respectto the second treatment plan. Accordingly, the systems and methodsdisclosed herein provide for simple, quick, efficient, and inexpensivecalibrations of image acquisition devices used to acquire test imagesbefore a treatment plan is applied to a patient.

The use of the various components of system 100 shown in FIG. 1 isdescribed in detail below. In general, a treatment plan may be appliedto a radiation detector, and thereby recorded on some medium or device,producing acquired image 112, such as is shown and described more fullywith reference to FIG. 1 below. Acquired image 112 is compared to a dosemap 106, also described more fully with reference to FIG. 1 below, thatrepresents the intensity of dosages planned as part of a treatment.

System Overview

FIG. 1 provides an overview of a system 100 used in at least oneembodiment to build an IMRT self calibration curve (ISCC). Treatmentplanning system 102 is any of a variety of treatment planning systemsknown to those skilled in the art, including but not limited to thePinnacle3 system manufactured by Phillips Medical Systems of Andover,Mass.; BrainSCAN, manufactured by Brainlab AG of Heimstetten, Germany;PLATO SunRise by Nucletron of Veenendaal, The Netherlands; Eclipse,manufactured by Varian Medical Systems of Palo Alto, Calif.

Treatment planning system 102 is used to create one or more radiationtreatment plans 104. Treatment planning system 102 is also used tocreate dose map 106, sometimes also referred to as the plan image. Suchuse of treatment planning system 102 will be well known to those skilledin the art. Further, those skilled in the art will recognize that dosemap 106 shows the expected distribution of the planned radiation dose ina quality assurance phantom or a patient. An example of a dose map 106is shown in FIG. 6A.

Radiation detector 108 is a device capable of detecting and receivingradiation such as will be known to those skilled in the art. In someembodiments, radiation detector 108 is a quality assurance phantom, alsoknown as a test phantom, such as will be known to those skilled in theart. The purpose of the test phantom is to emulate a medium that is toreceive a dose of radiation, such as human tissue.

Image acquisition device 110 may be any such device or medium as will beknown to those skilled in the art for recording detected radiation,including, but not limited to, radiographic film, a computed radiographydevice, an electronic portal imaging device, a charge-coupled device(CCD) camera, or BANG gel. Image acquisition device 110 produces one ormore acquired images 112. As described below, an acquired image 112 anddose map 106 are used to create ISCC curve 114. An example of anacquired image 112 is shown in FIG. 6B. As further described below, mostembodiments will create at least two ISCC curves 114 relating to atleast two treatment plans 104.

Those skilled in the art will recognize that the processes describedherein with reference to system 100 may be carried out by using one ormore computers such as are known to those skilled in the art and mayinclude any device or combination of devices capable of functioning asdescribed herein with respect to system 100, including receiving,outputting, processing, transforming, incorporating, and/or storinginformation. Accordingly, the processes described herein may be carriedout by the execution of computer-executable instructions embodied on acomputer-readable medium. For example, a computer used with system 100may be a general purpose computer capable of running a wide variety ofdifferent software applications. Further, such a computer may be aspecialized device limited to particular functions. In some embodiments,the computer is a network of computers. In general, system 100 mayincorporate a wide variety of different information technologyarchitectures. The computer is not limited to any type, number, form, orconfiguration of processors, memory, computer-readable mediums,peripheral devices, computing devices, and/or operating systems.

Further, some of the elements of system 100 may exist as representationswithin a computer. For example, treatment plan 104, dose map 106,acquired image 112, and/or ISCC curve 114 may exist as representationswithin one or more computers. Accordingly the computer may include or becoupled to interfaces and access devices for providing users (e.g., aradiological technician) with access to system 100. Thus, users are ableto access the processes and elements of system 100 using any accessdevices or interfaces known to those skilled in the art.

Initial Calibration Process

FIG. 2 describes a process flow for an initial calibration process. Step200 represents the process of obtaining a first acquired image 112 arepresenting a radiation distribution from a first radiation treatmentplan 104 a. The process represented in step 200 is described in detailwith reference to FIG. 3A. Step 202 represents the process of developinga calibration, e.g., a calibration curve or equation, that relates theradiation intensity distribution of acquired image 112 a to theradiation dose provided by the application of treatment plan 104 a. Asdiscussed above, various means, methods, and devices for performing thecalibration of step 202 will be known to those skilled in the art. Step204 represents the process of generating an ISCC curve 114 a. Theprocess represented in step 204 is described in detail with reference toFIG. 3B.

Image Acquisition Process

FIG. 3A is a flow diagram describing a process of acquiring images to beused in building an ISCC curve.

In step 300, a radiation treatment plan 104 is created. Creation ofradiation treatment plans is well known, and can be accomplished using avariety of known treatment planning systems 102. As is well known, aradiation treatment plan may include the intensity, duration, andlocation of radiation doses that will be delivered to a tumor siteduring a course of radiation therapy.

In step 302, treatment planning system 102 is used to create dose map106 associated with treatment plan 104, sometimes also referred to as aplan image.

In step 304, treatment plan 104 is applied to a radiation detector 108.The radiation distribution of the detected radiation is recorded onimage acquisition device 110. Image acquisition device 110 is used toproduce an acquired image 112, which represents the radiationdistribution produced from the application of treatment plan 104. Thoseskilled in the art will recognize that acquired image 112 may beproduced in a variety of ways. For instance, the example acquired image112 shown in FIG. 6B represents the scanned digital image of a qualityassurance film of an IMRT treatment field. In some embodiments acquiredimage 112 may be filtered, such as with a 5 by 5 median filter or someother filtering technique as may be known to those skilled in the art.Filtering may be used to reduce noise and/or to adjust for pixels orvoxels of different sizes between the treatment plan and the imageacquisition device.

ISCC Generation Process

Turning now to FIG. 3B, an ISCC generation process is described. Use ofthis ISCC generation process is discussed herein with respect to certainembodiments, but it should be understood that the process could beapplied to yet other embodiments that will become apparent to thoseskilled in the art upon reading this disclosure. In step 306, acquiredimage 112 is registered to dose map 106. Registering images refers tothe process of aligning images so that they occupy the same image space,and can then be compared and/or combined. Various methods and devicesfor registering images will be known to those skilled in the art, someof which are discussed in co-pending U.S. applications Ser. No.10/630,015 and U.S. application Ser. No. 11/009,602, filed Dec. 10,2004, entitled “OPTIMIZING IMAGE ALIGNMENT”.

In step 308, a common region of interest (ROI) is obtained with respectto dose map 106 and acquired image 112. The common ROI can be no largerthan the smaller of dose map 106 and acquired image 112, and is chosento exclude any extraneous non-dose related markings on acquired image112. For example, the ROI should not include any writing or fiducialmarkings, and should not include any areas that are off the edges ofdose map 106 or acquired image 112.

As part of step 308, various automated techniques known to those skilledin the art may be employed to exclude anomalous small areas of dose map106 and acquired image 112, such as areas where one image containspinpricks. Further, various known thresholding techniques may beemployed to exclude areas in selected dose ranges that are suspected ofhaving poor correlation. These might include low dose areas, highgradient areas, area close to physical media boundaries, etc.

In step 310, pixel values in dose map 106 are normalized to the maximumvalue of a pixel in dose map 106, and then are converted to percentagevalues if a “relative” measurement instead of an “absolute” measurementis desired.

In a first embodiment, at step 312 dose map 106 is divided into smallgeometric areas. An example of a dose map 106 divided into smallgeometric areas (and after a statistical function has been applied tothe areas, as described below) is shown in FIG. 7A. In one embodiment,the geometric areas are rectangles (or cubes for 3D images) that eachinclude one per cent of the area of the dose map 106. It should be notedthat the geometric areas of the image may or may not be contiguous andmay or may not physically overlap depending on the specific imagesemployed.

In a second embodiment, at step 312 the dose levels on dose map 106 aredivided into dose ranges. These dose ranges may or may not be contiguousand may or may not overlap. In one embodiment each dose range covers 1%of the total dose range on the plan image. That is, each 1% incrementcovers the range from 0 to the maximum dose on dose map 106 on a scaleof 0 to 100. For each range of pixels in the dose curve a statisticalmeasure such as the mean or the median is calculated. That is, theprocess finds all the pixels in each dose range on the plan image, i.e.,dose map 106, and takes the mean (or median or some other measure ofcentral tendency) of those pixels. An index locating the pixels on theregistered images in each range is maintained.

In yet a third embodiment, at step 312, dose map 106 is divided intosub-regions such as the geometric areas described above comprising apercentage of the area of dose map 106. Dose map 106 is then translatedinto what is referred to as “column format.” It should be understoodthat use of column format is optionally employed for the sake ofsimplifying the process but that steps 312 and the steps following step312 could be practiced without representing dose map 106 and acquiredimage 112 in column format. An example of a dose map 106 in columnformat is shown in FIG. 9A. Individual sub-regions of the dose map 106shown in FIG. 6A are represented in individual columns of the imageshown in FIG. 9A.

In the first embodiment discussed with reference to step 312, at step314 pixels are located on the acquired image 112 that correspond to eachgeometric area of dose map 106, defined as described above with respectto step 312. Dose map 106 and acquired image 112 may optionally betrimmed before corresponding pixels are located so that each of dose map106 and acquired image 112 have an area that is a whole number multipleof each geometric area. An acquired image 112 divided into geometricareas is shown in FIG. 7B. For each set of pixels so located, somestatistical measure or property is calculated. In some embodiments, forexample, those depicted in FIGS. 7A and 7B, the mean value of the pixelintensity is calculated for each set of located pixels. Otherembodiments may calculate the median or some other statistical propertysuch as will be known to those skilled in the art. For example, medianvalues preserve edges in images, while averaging tends to smooth theedges. The selection of the statistical property may depend on severalfactors which may include how fast the dose changes within a region.

At step 314, in the second embodiment discussed with reference to step312, pixels are located in the acquired image 112 corresponding to doseranges identified in dose map 106 as described above with respect tostep 312. A statistical measure (e.g., mean, median etc.) of each doserange in the acquired image 112 is then taken as described above withrespect to dose map 106 in step 312.

In the third embodiment discussed above with reference to step 312, atstep 314 acquired image 112 is divided into sub-regions and thenrepresented in column format as shown in FIG. 9B. For each suchsub-region a correlation is made between the pixels in the referenceimage, i.e., dose map 106, and the pixels in the corresponding geometricareas in the acquired image 112. Such correlations are known to thoseskilled in the art. For example, FIG. 10 shows a graph representing thecorrelation coefficient, such as will be known to those skilled in theart, for each set of corresponding columns (numbered 1 through 100) inthe mages shown in FIGS. 9A and 9B. The corresponding sub-regionsrepresented by the corresponding columns are ranked in order of themeasure of correlation. FIGS. 11A and 11B show images in column formatrepresenting respectively a dose map 106 and an acquired image 112 inwhich the columns are sorted according to the value of a correlationcoefficient. Starting with the most highly correlated areas thecorresponding pixels are used to develop a calibration curve in a mannersimilar to that described in the preceding paragraph.

In some embodiments a correlation threshold is established such thatonly columns whose correlation exceeds a predetermined threshold areconsidered when building an ISCC curve. For example, with reference toFIGS. 11A and 11B, columns 1-33 of the column images have correlationsless than or equal to −0.97, the value −0.97 having been selected as thecorrelation threshold. Accordingly, in this example, only columns 1-33are to be considered when building the ISCC curve.

In step 316, a raw ISCC curve is developed. An example of an ISCC curveproduced in one practiced embodiment is shown in FIG. 4. The ISCC curveof this embodiment plots a value representing the intensity of the doseof treatment plan 104 for each of the ranges of dose map 106 defined instep 312 against a value representing the pixel intensity in each of thecorresponding pixels in the acquired image 112, this value being relatedto whatever statistical measure was selected in step 314. Anotherexample of a raw ISCC curve is provided in FIG. 8A. The raw ISCC curveshown in FIG. 8A was developed by dividing the dose map 106 shown inFIGS. 6A and 7A and the acquired image shown in FIGS. 7A and 7B intosmall geometric areas. FIG. 12A shows a raw ISCC curve based on plottingpixel values for areas of the dose map 106 and acquired image 112 thatexceed the correlation threshold discussed above with respect to step314.

In step 318, the ISCC curve developed in step 318 is post-processed toensure that pixel values monotonically decrease as dose values rise. Inaddition, other post-processing techniques such as will be known tothose skilled in the art may be applied. For example, techniques tosmooth or fit the ISCC curve may be applied in step 318. FIG. 8B showsan ISCC curve resulting from post-processing the raw ISCC curve shown inFIG. 8A. FIG. 12B shows an ISCC curve resulting from post-processing theraw ISCC curve shown in FIG. 12A.

Subsequent Calibration Process

FIG. 5 describes a subsequent calibration process, i.e., a relativecalibration performed for a treatment plan 104 b other than thetreatment plan 104 a for which the calibration curve was developed instep 202 of the initial calibration process described above withreference to FIG. 2. Treatment plan 104 b may be referred to as a secondor subsequent treatment plan.

In step 502, an image acquisition process is performed with respect to asubsequent treatment plan 104 b. Step 502 includes performing the stepsdescribed above with reference to FIG. 3A for treatment plan 104 b.Accordingly, step 502 produces a dose map 106 b and an acquired image112 b.

In step 504, the ISCC generation process described above with referenceto FIG. 3B is performed with respect to dose map 106 b and acquiredimage 112 b. Accordingly, step 504 produces a subsequent ISCC curve 114b.

In step 506, a comparison is made between the first ISCC curve 114 a tothe subsequent ISCC curve 114 b and identifies differences, or fit,between the two curves. In step 508, the subsequent acquired image 112 bis modified based on the differences or fits identified between thefirst ISCC curve 114 a and the subsequent ISCC curve 114 b. Theobjective of this modification is to transform the acquired image 112 bto a state in which it can be calibrated using the calibration curvedeveloped in step 202 described above with reference to FIG. 2. Thetransformation of acquired image 112 b may be preformed using a varietyof methods known to those skilled in the art including, for example,those discussed in U.S. Pat. No. 6,528,803. The relationship between thetwo curves, or sets of points, may be as simple as a difference, butcould also be more complex and can take the form of look-up tables orcurve fits such as will be known to those skilled in the art.

In step 510, calibration curve developed in step 202 described abovewith reference to FIG. 2 is applied to acquired image 112 b.

Evaluation of Experimental Calibration

The ISCC curves developed as described above can advantageously be usedto evaluate the usefulness of a calibration curve that is experimentallyderived using methods known to those skilled in the art. Accordingly, insome embodiments the ISCC may be compared to an experimentally obtainedor calculated calibration curve. The correlation or correspondencebetween the ISCC and experimentally obtained curves is a measure of theability of the experimentally derived calibration curve to successfullymodel a dose distribution such as is represented by a dose map 106 andis shown on an acquired image 112. Those skilled in the art willrecognize that it is possible to establish a threshold for acceptance onan experimentally derived calibration curve to prevent curves withexcessive errors from being used. The user can also use thecorrespondence between the ISCC curve and the experimentally derivedcurve to determine whether discrepancies between dose maps 106 andacquired images 112 are due to calibration errors, TPS modeling errorsor radiation delivery errors.

Evaluation and Selection of Normalization Values

The ISCC curves developed as described above further can advantageouslybe used to evaluate and select normalization values for images, such asacquired images 112 and dose map 106, to be compared for qualityassurance purposes. In systems and methods for relative dosimetry suchas those newly disclosed herein, it is generally required to normalizethe pixel values on the plan and acquired images so that they are scaledover a similar range. The selection of these normalization values canoften be difficult. For example, if experimental calibration and ISCCcurves differ in shape, then optimizing the normalization at one doselevel can compromise agreement at other dose levels. Changing thenormalization value on the plan image, i.e., dose map 106, will displacethe ISCC curve generated between the normalized plan image and theacquired image 112. Accordingly, the agreement between the ISCC curveand the experientially derived curve can either be optimized over theentire curve or for selected ranges or points of the curve by varyingthe normalization value. In this manner an optimized normalization valuecan be achieved for different criteria.

FIG. 13 shows a graph 1300 including an experimentally derivedcalibration curve 1310, i.e., a calibration curve that was produced inways known to those skilled in the art or according to the ISCCgeneration process newly disclosed above. FIG. 13 also shows an ISCCcurve 1320 that was produced by dividing a dose map 106 and an acquiredimage 112 into dose ranges as described above with respect to FIG. 3.There are many ways to compare and normalize curves 1310 and 1320. Forexample, one way to compare curves 1310 and 1320 is to evaluate thedifference between the curves. For purposes of the present example,curves 1310 and 1320 will be evaluated for a selected range of pixelvalues, generally a common set of pixel values that comprise the commonpixel value range of the two curves 1310 and 1320. In this case eachpixel value in the selected range will be linearly interpolated, butthose skilled in the art will recognize that there are a variety of waysin which this interpolation could be performed.

Looking at the dose differences between the two curves 1310 and 1320,plotted on graph shown in FIG. 14, one can discern that if one were touse the experimental calibration curve 1310 to calibrate an acquiredimage 112 from pixel values to dose levels, and then compare thatacquired image 112 to a dose map 106 for quality assurance purposes, onewould probably see an over-response in the low dose regions (0-10 cGy)of the calibration curve 1310, and an under-response in the 10-30 cGyrange of the calibration curve 1310. Looking at the plot shown in FIG.14 in combination with the plot shown in FIG. 13, the observer mightconclude that an additional experimental calibration is needed forpoints in the 0-10 cGy range to better match the ISCC curve 1320. Afterperforming an additional experimental calibration, one could repeat theanalysis described with respect to FIGS. 13 and 14 to see if differencesbetween the ISCC curve 1320 and an experimental calibration curve 1310had improved.

It is also possible that, instead of looking at particular dose regions,as is described with respect to FIG. 14, curves 1310 and 1320 could becompared in their entirety. For example, the correlation of the twocurves could be evaluated, or the Root Mean Square (RMS) of thedifference could be calculated. One could set an acceptance thresholdfor these parameters in order to accept or reject the experimental curve1310 for use in calibrating dosimetry devices. Alternatively one couldevaluate sections of the curve 1310 individually one were particularlyinterested in certain regions of the curve (e.g., high dose regions). Ifcurve fitting were being used, this would be called a Spline fit. Forexample the curve 1310 could be divided into 10 equal sections andstatistics, such as a correlation as shown in FIG. 15A or RMS as shownin FIG. 15B, calculated for each.

Further, as mentioned above, a normalization value is often applied toadjust a calibration curve to reduce systemic errors between imagesbeing compared, e.g., a dose map 106 and an acquired image 112. Forexample, one could normalize the ISCC curve 1320 and the experimentalcurve 1310 to their maximums. The experimental dose curve 1310 may thenbe adjusted by a range of factors, after which one may plot the RMS ofthe adjusted experimental curve 1310 against the ISCC curve 1320. Then,by estimating the lowest point on this plot, an optimal normalizationfactor may be determined. Note that this technique could be performedwith respect to a portion of the curve 1310, allowing, for example,optimization for high doses, as is shown in FIG. 16. The lowest point inthe curve shown in FIG. 16 appears to be about 1.01, suggesting that1.01 is an optimal normalization factor for the region shown in FIG. 16.Those skilled in the art will understand that this factor could befurther refined by fitting the curve to a polynomial equation.

Conclusion

The above description is intended to be illustrative and notrestrictive. Many embodiments and applications other than the examplesprovided would be apparent to those of skill in the art upon reading theabove description. The scope of the invention should be determined, notwith reference to the above description, but should instead bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled. It isanticipated and intended that future developments will occur incalibrating dosimetry images, and that the invention will beincorporated into such future embodiments.

1-20. (canceled)
 21. A method comprising: creating a firstself-calibration curve that relates a first dose map associated with afirst radiation treatment plan to a first acquired image recorded froman application of the first radiation treatment plan; and creating atleast one subsequent self-calibration curve that relates at least onesubsequent dose map associated with at least one subsequent radiationtreatment plan to at least one subsequent acquired image recorded fromapplication of the at least one subsequent radiation treatment plan. 22.The method of claim 21, further comprising comparing the firstself-calibration curve to the at least one subsequent self-calibrationcurve.
 23. The method of claim 22, further comprising modifying the atleast one subsequent acquired image based on the comparison of the firstself-calibration curve to the at least one subsequent self-calibrationcurve.
 24. The method of claim 23, further comprising generating aninitial calibration based on the first dose map and the application ofthe first radiation treatment plan.
 25. The method of claim 24, furthercomprising applying the initial calibration to the modified at least onesubsequent acquired image.
 26. The method of claim 23, wherein saidcomparing step includes determining at least one difference or fitbetween the first self-calibration curve and the at least one subsequentself-calibration curve, said modifying step including modifying the atleast one subsequent acquired image based on the at least one differenceor fit.
 27. The method of claim 21, wherein said creating steps includedividing each of the dose maps and each of the acquired images into aplurality of geometric areas or a plurality of dose ranges.
 28. Themethod of claim 21, further comprising representing each of the dosemaps and the acquired images in column format.
 29. The method of claim21, further comprising using a correlation threshold to determineportions of each of the dose maps and portions of each of the acquiredimages to be used in creating each of the self-calibration curves.
 30. Acomputer-readable medium tangibly embodying computer-readableinstructions configured to instruct one or more processors to performsteps comprising: creating a first self-calibration curve that relates afirst dose map associated with a first radiation treatment plan to afirst acquired image recorded from an application of the first radiationtreatment plan; and creating at least one subsequent self-calibrationcurve that relates at least one subsequent dose map associated with atleast one subsequent radiation treatment plan to at least one subsequentacquired image recorded from application of the at least one subsequentradiation treatment plan.
 31. The computer-readable medium of claim 30,further comprising computer-readable instructions configured to instructthe one or more processors to perform a step of comparing the firstself-calibration curve to the at least one subsequent self-calibrationcurve.
 32. The computer-readable medium of claim 31, further comprisingcomputer-readable instructions configured to instruct the one or moreprocessors to perform a step of modifying the at least one subsequentacquired image based on the comparison of the first self-calibrationcurve to the at least one subsequent self-calibration curve.
 33. Thecomputer-readable medium of claim 32, further comprisingcomputer-readable instructions configured to instruct the one or moreprocessors to perform a step of generating an initial calibration basedon the first dose map and the application of the first radiationtreatment plan.
 34. The computer-readable medium of claim 33, furthercomprising computer-readable instructions configured to instruct the oneor more processors to perform a step of applying the initial calibrationto the modified at least one subsequent acquired image.
 35. Thecomputer-readable medium of claim 32, wherein said comparing stepincludes determining at least one difference or fit between the firstself-calibration curve and the at least one subsequent self-calibrationcurve, said modifying step including modifying the at least onesubsequent acquired image based on the at least one difference or fit.36. The computer-readable medium of claim 30, wherein said creatingsteps include dividing each of the dose maps and each of the acquiredimages into a plurality of geometric areas or a plurality of doseranges.
 37. The computer-readable medium of claim 30, further comprisingcomputer-readable instructions configured to instruct the one or moreprocessors to perform a step of representing each of the dose maps andthe acquired images in column format.
 38. The computer-readable mediumof claim 30, further comprising computer-readable instructionsconfigured to instruct the one or more processors to perform a step ofusing a correlation threshold to determine portions of each of the dosemaps and portions of each of the acquired images to be used in creatingeach of the self-calibration curves.
 39. A system comprising: means forcreating a first self-calibration curve that relates a first dose mapassociated with a first radiation treatment plan to a first acquiredimage recorded from an application of the first radiation treatmentplan; and means for creating at least one subsequent self-calibrationcurve that relates at least one subsequent dose map associated with atleast one subsequent radiation treatment plan to at least one subsequentacquired image recorded from application of the at least one subsequentradiation treatment plan.
 40. The system of claim 39, further comprisingmeans for comparing the first self-calibration curve to the at least onesubsequent self-calibration curve.
 41. The system of claim 40, furthercomprising means for modifying the at least one subsequent acquiredimage based on the comparison of the first self-calibration curve to theat least one subsequent self-calibration curve.
 42. The system of claim41, further comprising means for generating an initial calibration basedon the first dose map and the application of the first radiationtreatment plan.
 43. The system of claim 42, further comprising means forapplying the initial calibration to the modified at least one subsequentacquired image.
 44. The system of claim 41, wherein said means forcomparing includes means for determining at least one difference or fitbetween the first self-calibration curve and the at least one subsequentself-calibration curve, said means for modifying including modifying theat least one subsequent acquired image based on the at least onedifference or fit.
 45. The system of claim 39, wherein each said meansfor creating includes means for dividing the dose map and the acquiredimage into a plurality of geometric areas or a plurality of dose ranges.46. The system of claim 39, wherein each of the dose maps and theacquired images is represented in column format.
 47. The system of claim39, further comprising means for using a correlation threshold todetermine portions of each of the dose maps and portions of each of theacquired images to be used in creating each of the self-calibrationcurves.